The disclosure relates to detecting and addressing operations, system and application performance anomalies in computing resources. More particularly, the methods and systems described herein relate to functionality for anomaly identification and information technology infrastructure resource optimization.
Managing conventional data centers and applications in information technology (IT) operations is increasingly challenging. Complexity in conventional IT operations is escalating, typical IT staffs are overwhelmed, and IT budgets struggle to keep pace with increasing demand. Typically, managers of IT resources have limited evaluation methods to track the by-minute performance and resource consumption of their information technology infrastructure assets, including real and virtual servers, memory devices, input/output (I/O) systems, network systems, operating systems, and application software for machine (including server) level operational efficiency, capacity, and utility. The results of current monitoring and evaluation methods typically fail to include identifying coding errors, rogue software, application and system inefficiencies, and other anomalies. Such anomalies and inefficiencies can consume much of the capacity and/or utility of IT assets, wasting their use, resulting in over-resourcing/over-purchasing of assets to meet needs that appear to require new resources because utilization is being wasted on activity that is not real work, and leading to delays in system access and loss of user-level productivity due to time delays in accessing system information. In addition, these same machine inefficiencies may lead to delays in IT project execution and to system failures, including outages that threaten business operations dependent on IT assets.